Parameter Estimation of PV Solar Cells and Modules Using Deep Learning-Based White Shark Optimizer Algorithm

Abstract

Photovoltaic systems are affected by light intensity, temperature, and radiation angle, which influence their efficiency. Accurate estimation of PV module parameters is essential for improving performance. This paper presents an improved optimization technique based on the White Shark Optimizer (WSO) algorithm to optimize key characteristics of the PV module, including current, voltage, series resistance, shunt resistance, and ideality factor. The proposed method incorporates opposition-based learning (OBL) and chaos theory to improve search efficiency. A critical aspect of PV module modeling is inherent symmetry in electrical and thermal characteristics, where balanced parameter estimation ensures uniform energy conversion efficiency. With the application of symmetrical search techniques during the process of optimization, the proposed method enhances convergence robustness and stability, ensuring consistent and precise results across different PV models. Experimental evaluations conducted on three PV models—Single Diode Model (SDM), Double Diode Model (DDM), and general photovoltaic modules—demonstrate that the proposed method outperforms existing metaheuristic techniques such as Jumping Spider Optimization (JSO), Harris Hawks Optimization (HHO), WOA, Gray Wolf Optimizer (GWO), and basic WSO. Key results show improvements in the Friedman rating by 8.1%, 10.79%, and 9.6% for the SDM, DDM, and PV modules, respectively. Additionally, the proposed method achieves superior parameter estimation accuracy, as evidenced by reduced RMSE values compared to the competing algorithms. This work highlights the importance of advanced optimization techniques in maximizing PV output power while maintaining symmetry in parameter estimation. By ensuring a balanced and systematic optimization approach, this study assists in the development of robust and efficient solutions for PV system modeling.